{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "specific-closure",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "endangered-disco",
   "metadata": {},
   "outputs": [],
   "source": [
    "shared_dir = \"/gpfs/scratch/chgwang/XI/DataBase/Model_figure\"\n",
    "csv_path = \"/gpfs/scratch/chgwang/XI/DataBase/Model_1d_20/evaluation.csv\"\n",
    "df = pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "august-implementation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df.index, df.loc[:, \"tra_acc\"], label=\"training accuracy\")\n",
    "plt.plot(df.index, df.loc[:, \"val_acc\"], label=\"validation accuracy\")\n",
    "plt.xticks(np.arange(0,21,5))\n",
    "plt.title(\"model accuracy\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend()\n",
    "fig_path = os.path.join(shared_dir, \"model_accuracy.png\")\n",
    "plt.savefig(fig_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sunset-reading",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.figure()\n",
    "plt.plot(df.index, df.loc[:, \"tra_loss\"], label=\"training loss\")\n",
    "plt.plot(df.index, df.loc[:, \"val_loss\"], label=\"validation loss\")\n",
    "plt.xticks(np.arange(0,21,5))\n",
    "plt.title(\"model loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend()\n",
    "fig_path = os.path.join(shared_dir, \"model_loss.png\")\n",
    "plt.savefig(fig_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "hundred-prison",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tra_acc</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>tra_loss</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.279488</td>\n",
       "      <td>0.283826</td>\n",
       "      <td>0.449237</td>\n",
       "      <td>0.422228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.850465</td>\n",
       "      <td>0.830404</td>\n",
       "      <td>0.173294</td>\n",
       "      <td>0.172566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.863929</td>\n",
       "      <td>0.817067</td>\n",
       "      <td>0.141240</td>\n",
       "      <td>0.164790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.906066</td>\n",
       "      <td>0.878646</td>\n",
       "      <td>0.106656</td>\n",
       "      <td>0.121018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.912333</td>\n",
       "      <td>0.867029</td>\n",
       "      <td>0.102726</td>\n",
       "      <td>0.132638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.921524</td>\n",
       "      <td>0.893787</td>\n",
       "      <td>0.089971</td>\n",
       "      <td>0.106982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.926118</td>\n",
       "      <td>0.893917</td>\n",
       "      <td>0.077147</td>\n",
       "      <td>0.101618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.917338</td>\n",
       "      <td>0.887733</td>\n",
       "      <td>0.078454</td>\n",
       "      <td>0.091403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.946760</td>\n",
       "      <td>0.936254</td>\n",
       "      <td>0.076052</td>\n",
       "      <td>0.102254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.911646</td>\n",
       "      <td>0.886021</td>\n",
       "      <td>0.090018</td>\n",
       "      <td>0.118128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.947410</td>\n",
       "      <td>0.939146</td>\n",
       "      <td>0.066895</td>\n",
       "      <td>0.093123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.946334</td>\n",
       "      <td>0.927465</td>\n",
       "      <td>0.069706</td>\n",
       "      <td>0.101694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.946698</td>\n",
       "      <td>0.910528</td>\n",
       "      <td>0.072892</td>\n",
       "      <td>0.101046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.944066</td>\n",
       "      <td>0.905673</td>\n",
       "      <td>0.073040</td>\n",
       "      <td>0.118495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.953674</td>\n",
       "      <td>0.941639</td>\n",
       "      <td>0.062470</td>\n",
       "      <td>0.083125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.948715</td>\n",
       "      <td>0.912100</td>\n",
       "      <td>0.062531</td>\n",
       "      <td>0.111906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.946513</td>\n",
       "      <td>0.936337</td>\n",
       "      <td>0.078529</td>\n",
       "      <td>0.090597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.958519</td>\n",
       "      <td>0.941527</td>\n",
       "      <td>0.060907</td>\n",
       "      <td>0.092395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.958640</td>\n",
       "      <td>0.937826</td>\n",
       "      <td>0.056853</td>\n",
       "      <td>0.106516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.954205</td>\n",
       "      <td>0.939090</td>\n",
       "      <td>0.059095</td>\n",
       "      <td>0.089775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tra_acc   val_acc  tra_loss  val_loss\n",
       "0   0.279488  0.283826  0.449237  0.422228\n",
       "1   0.850465  0.830404  0.173294  0.172566\n",
       "2   0.863929  0.817067  0.141240  0.164790\n",
       "3   0.906066  0.878646  0.106656  0.121018\n",
       "4   0.912333  0.867029  0.102726  0.132638\n",
       "5   0.921524  0.893787  0.089971  0.106982\n",
       "6   0.926118  0.893917  0.077147  0.101618\n",
       "7   0.917338  0.887733  0.078454  0.091403\n",
       "8   0.946760  0.936254  0.076052  0.102254\n",
       "9   0.911646  0.886021  0.090018  0.118128\n",
       "10  0.947410  0.939146  0.066895  0.093123\n",
       "11  0.946334  0.927465  0.069706  0.101694\n",
       "12  0.946698  0.910528  0.072892  0.101046\n",
       "13  0.944066  0.905673  0.073040  0.118495\n",
       "14  0.953674  0.941639  0.062470  0.083125\n",
       "15  0.948715  0.912100  0.062531  0.111906\n",
       "16  0.946513  0.936337  0.078529  0.090597\n",
       "17  0.958519  0.941527  0.060907  0.092395\n",
       "18  0.958640  0.937826  0.056853  0.106516\n",
       "19  0.954205  0.939090  0.059095  0.089775"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "electric-opinion",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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